基于机器学习的钢水精炼智能控制方法、装置、设备及介质
By employing machine learning-based intelligent control methods, utilizing gradient boosting trees and recurrent neural network models, real-time and precise regulation of the steel refining process is achieved. This solves the problems of large errors, high labor intensity, and poor process coordination caused by manual adjustment, thereby improving the stability of steel quality and production efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUNAN RUILING TECH CO LTD
- Filing Date
- 2026-05-06
- Publication Date
- 2026-07-17
AI Technical Summary
The steel refining process suffers from problems such as large errors in manual adjustment, high labor intensity, poor process coordination, and low data utilization, making it difficult to achieve precise control and stable production.
A machine learning-based intelligent control method is adopted. By collecting multi-source process data, a gradient boosting tree and a recurrent neural network model are constructed to realize real-time evaluation of the molten steel state and multi-task intelligent control, and generate collaborative control commands to drive the power supply heating and alloy feeding system.
It improves automation, reduces the intensity of manual intervention, enhances the stability of molten steel quality and refining efficiency, optimizes process synergy, improves data utilization, and achieves full-process traceability.
Smart Images
Figure CN122128489B_ABST